R Notebook
Step 1: revised catch for 2010-2020 Step 2: tune resilience of all the species (i.e., long-lived, slower growing species = vulnerable…etc…) Step 3: calibrated model with fishing mortality represented (time-averaged catch comparison, model vs observed) = base model Step 4: check base model without fishing to ensure co-existence is still present, observed biomass would not be specified Step 5: Experiments through time - effort functions - uses the base model with no fishing effort as a starting point Step 6: Expose the base model to fishing through time and compare to observed catch time series - start from 0 effort at first time-step to max effort approx around collapse - If the modelled versus observed don’t match, need to adjust effort/or initial biological params and experiment to see what improves - hand tune / optim options Adjust and recalibrate if needed (optim) Use rules to constrain - i.e., - time-averaged biomasses within calibration period must be +- xx % - can optim things like fishing-size selectivity, PPMRs etc
Can we reproduce the time series?
Load libraries
remotes::install_github("sizespectrum/mizerExperimental")Skipping install of 'mizerExperimental' from a github remote, the SHA1 (8279ac0d) has not changed since last install.
Use `force = TRUE` to force installationlibrary(mizerExperimental)
# remotes::install_github("sizespectrum/mizerMR")
# library(mizerMR)
# library(mizer)
library(tidyverse)
library(tictoc)
library(parallel)
# library(plotly)Catch data for all indivudals and summarised as totals per species each year
Maybe this isn’t expressing effort, rather fishing mortality
df_ind_CPUE <- readRDS("ind_catch_weight_BanzareBank_1930_2019_CPUE.rds")
df_CPUE_kg_day <- readRDS("catch_timeseries_BanzareBank_1930_2019_CPUE.rds")
glimpse(df_ind_CPUE)Rows: 107,032
Columns: 9
Groups: Exp.no, Species [104]
$ ID <int> 81681, 81687, 81689, 104868, 104869, 104879, 104881, 104886, 104907, 104908, 104909, 104910, 104916, 104921, 104959, 104960, 104963, 104981, 104984, 104985, 104986, 104987, …
$ Exp.no <int> 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781,…
$ Year <int> 1932, 1932, 1932, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933,…
$ Species <chr> "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "S…
$ Length <dbl> 1737.36, 1493.52, 1645.92, 1645.92, 1584.96, 1493.52, 1645.92, 1615.44, 1706.88, 1554.48, 1615.44, 1645.92, 1645.92, 1706.88, 1676.40, 1615.44, 1615.44, 1584.96, 1615.44, 15…
$ Length.unit <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
$ Weight_kg <dbl> 57160.49, 36312.79, 48601.83, 48601.83, 43399.17, 36312.79, 48601.83, 45951.43, 54204.52, 40943.21, 45951.43, 48601.83, 48601.83, 54204.52, 51352.25, 45951.43, 45951.43, 433…
$ date <date> 1932-12-10, 1932-12-10, 1932-12-10, 1933-12-12, 1933-12-12, 1933-12-12, 1933-12-12, 1933-12-13, 1933-12-14, 1933-12-14, 1933-12-14, 1933-12-14, 1933-12-14, 1933-12-15, 1933…
$ Duration <int> 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 1…glimpse(df_CPUE_kg_day)Rows: 132
Columns: 5
Groups: Year [74]
$ Year <int> 1930, 1931, 1932, 1932, 1933, 1933, 1934, 1934, 1935, 1935, 1936, 1936, 1937, 1937, 1938, 1938, 1939, 1939, 1940, 1940, 1946, 1946, 1947, 1947, 1948, 1948, 1949, 1949, 19…
$ Species <chr> "Baleen", "Baleen", "Baleen", "Sperm", "Baleen", "Sperm", "Baleen", "Sperm", "Baleen", "Sperm", "Baleen", "Sperm", "Baleen", "Sperm", "Baleen", "Sperm", "Baleen", "Sperm"…
$ total_catch_kg <dbl> 50155688.13, 335374080.08, 306611735.04, 142075.12, 496515326.66, 3108915.83, 279492543.54, 5993267.00, 220991003.48, 3701390.97, 184531268.07, 4019223.65, 198881583.46, …
$ effort_days <dbl> 79.5265942, 373.4554741, 260.8692295, 0.7931792, 471.8601991, 16.4294765, 309.9332648, 26.8628395, 241.8924453, 18.4724967, 211.9265770, 18.3120140, 218.3889041, 17.84867…
$ CPUE <dbl> 630678.186, 898029.627, 1175346.497, 179121.092, 1052250.916, 189227.930, 901782.981, 223106.236, 913592.002, 200373.076, 870732.074, 219485.615, 910676.228, 206482.577, …Yield for the period that matches Ecopath model, post 2000 annual average
df_yield_2000 <- df_CPUE_kg_day %>%
filter(Year > 2009) %>%
group_by(Species) %>%
summarise(yield = sum(total_catch_kg)/10)
df_yield_2000effort_days/max value plotted by species This might indicate the effort curve required, especially if there is a difference between different species
Relative change in catch pre-1930, could inform the relative change in effort Ask Cami, how could I reconstruct the effort to the initial point of catch when effort = 0…i.e., approx 1850
df_plot %>%
ggplot(aes(x = Year, y = effort_standard, colour = Species)) +
geom_smooth() +
geom_line()
# facet_wrap(~Species) 
Load catch length
catch_lengths <- readRDS("catch_lengths.rds")
glimpse(catch_lengths)Rows: 355
Columns: 5
Groups: Species [4]
$ Species <chr> "Antarctic Minke", "Antarctic Minke", "Antarctic Minke", "Antarctic Minke", "Antarctic Minke", "Antarctic Minke", "Antarctic Minke", "A…
$ species <chr> "minke whales", "minke whales", "minke whales", "minke whales", "minke whales", "minke whales", "minke whales", "minke whales", "minke …
$ catch <int> 1, 5, 5, 9, 16, 17, 27, 30, 41, 36, 46, 43, 26, 33, 26, 32, 34, 38, 55, 65, 95, 69, 112, 197, 149, 167, 390, 239, 305, 614, 503, 503, 1…
$ length <dbl> 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710, 720, 730, 740, …
$ dl <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,…The value for euphausiids obs_yield is from
calibration_catch_histsoc_1850_2004_regional_models.csv
found at http://portal.sf.utas.edu.au/thredds/catalog/gem/fishmip/ISIMIP3a/InputData/fishing/histsoc/catalog.html.
It is the annual average yield over a 22 year period for the Prydz Bay
Region.
The values of obs_yield for minke whales (2175476136),
orca (19923729), sperm whales (4062177445), and baleen whales
(50341696055) are the annual average yield from 1930 - 2019 from IWC
records of whaling in the Prydz Bay model domain (1,433,028 km2).
Adjust the catch to represent the 2010 - 2020 time period + incorporate toothfish catch if possible (i.e., Stacey’s paper values)
df_ind_CPUE <- readRDS("ind_catch_weight_BanzareBank_1930_2019_CPUE.rds")
df_CPUE_kg_day <- readRDS("catch_timeseries_BanzareBank_1930_2019_CPUE.rds")
glimpse(df_ind_CPUE)Rows: 107,032
Columns: 9
Groups: Exp.no, Species [104]
$ ID <int> 81681, 81687, 81689, 104868, 104869, 104879, 104881, 104886, 104907, 104908, 104909, 104910, 104916, 104921, 104959, 104960, 104963…
$ Exp.no <int> 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781, 5781,…
$ Year <int> 1932, 1932, 1932, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933, 1933,…
$ Species <chr> "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sperm", "Sper…
$ Length <dbl> 1737.36, 1493.52, 1645.92, 1645.92, 1584.96, 1493.52, 1645.92, 1615.44, 1706.88, 1554.48, 1615.44, 1645.92, 1645.92, 1706.88, 1676.…
$ Length.unit <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
$ Weight_kg <dbl> 57160.49, 36312.79, 48601.83, 48601.83, 43399.17, 36312.79, 48601.83, 45951.43, 54204.52, 40943.21, 45951.43, 48601.83, 48601.83, 5…
$ date <date> 1932-12-10, 1932-12-10, 1932-12-10, 1933-12-12, 1933-12-12, 1933-12-12, 1933-12-12, 1933-12-13, 1933-12-14, 1933-12-14, 1933-12-14…
$ Duration <int> 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,…glimpse(df_CPUE_kg_day)Rows: 132
Columns: 5
Groups: Year [74]
$ Year <int> 1930, 1931, 1932, 1932, 1933, 1933, 1934, 1934, 1935, 1935, 1936, 1936, 1937, 1937, 1938, 1938, 1939, 1939, 1940, 1940, 1946, 19…
$ Species <chr> "Baleen", "Baleen", "Baleen", "Sperm", "Baleen", "Sperm", "Baleen", "Sperm", "Baleen", "Sperm", "Baleen", "Sperm", "Baleen", "Sp…
$ total_catch_kg <dbl> 50155688.1, 335374080.1, 306611735.0, 142075.1, 496515326.7, 3108915.8, 279492543.5, 5993267.0, 220991003.5, 3701391.0, 18453126…
$ effort_days <dbl> 79.5265942, 373.4554741, 260.8692295, 0.7931792, 471.8601991, 16.4294765, 309.9332648, 26.8628395, 241.8924453, 18.4724967, 211.…
$ CPUE <dbl> 630678.2, 898029.6, 1175346.5, 179121.1, 1052250.9, 189227.9, 901783.0, 223106.2, 913592.0, 200373.1, 870732.1, 219485.6, 910676…Yield for the period that matches Ecopath model, post 2000 annual average
df_yield_2000_2019 <- df_CPUE_kg_day %>%
filter(Year > 2000) %>%
group_by(Species) %>%
summarise(yield = sum(total_catch_kg)/19)
df_yield_2000_2019Add yield in tonnes per km2, same as g m2 and it is consistent with
biomass_observed
1.474341e+12 m^2 for model domain
1.474341e+12/1e+6 = 1474341 km^2
507511 kg of minke whale per year from 2000 - 2019
507.5/1474341 = 0.0003442216 tonnes Minke whale yield per km2 from 2000 - 2019
15619.24 kg of baleen whale per year from 2000 - 2019
15.6/1474341 = 1.0581e-05 tonnes of baleen whale per km2 from 2000 - 2019
obs_yield <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0003442216, 0, 0, 1.0581e-05) # to add a yield_observed column in species_params
biomass_cutoff <- c(0.1, 1, 1, 1, 40, 4500, 1, 1, 200000, 10000, 135000, 600000, 490000, 3650000, 2250000) # to add a biomass_cutoff column in species_params
Steady-state model with no fishing effort calibrated to observed biomass values from McCormack et al. 2020 that represent an average state of the food web from 2010-2020.
# params <- readRDS("stage1_steady_vXX.rds") # Incorrect orca and leopard seal w_max values
so_params <- readRDS("params_16_March_2023.rds") # Updated w_max for orca and leopard seals
# params <- setParams(setFishing(params, initial_effort = 0.2))
# species_params(params)$yield_observed <- obs_yield
# species_params(params)$biomass_cutoff <- biomass_cutoff
# species_params(params)$biomass_cutoffLow <- biomass_cutoff
# species_params(params)$biomass_cutoffHigh <- species_params(params)$w_max
species_params(so_params)$yield_observed <- obs_yield
species_params(so_params)$biomass_cutoff <- biomass_cutoff
species_params(so_params)$biomass_cutoffLow <- biomass_cutoff
species_params(so_params)$biomass_cutoffHigh <- species_params(so_params)$w_max
# species_params(params) |> dplyr::select(species, yield_observed, w_mat, w_max, R_max)
species_params(so_params) |> dplyr::select(species, yield_observed, w_mat, w_max, R_max)# groups |> dplyr::select(species, yield_observed, biomass_observed, biomass_cutoff, w_min)# yield_observed <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0003442216, 0, 0, 1.0581e-05) # to add a yield_observed column in species_params
#
# species_params(params)$yield_observed <- yield_observed# biomass_cutoff <- c(0.1, 1, 1, 1, 40, 4500, 1, 1, 200000, 10000, 135000, 600000, 490000, 3650000, 2250000) # to add a biomass_cutoff column in species_params
#
# species_params(params)$biomass_cutoff <- biomass_cutoffUpdate max and mat size for orca using sealifebase values rather than the previous w_max of 6t
# species_params(params)$species[13]
# species_params(params)$w_max[13]
# species_params(params)$w_mat[13]
#
# species_params(params)$w_max[13] <- 10628034
#
# species_params(params)$w_mat[13] <- 3198855
#
# species_params(params)$w_max[13]
# species_params(params)$w_mat[13]
# species_params(params)$w_mat25[13]Update max and mat size for leopard seal using sealifebase values. Max weight observed reported as 450kg. Using the sealifebase values for L-W conversion and max length results in an estimated max weight of 545875.2g, which is too high above the max weight observed.
# species_params(params)$species[9]
# species_params(params)$w_max[9]
# species_params(params)$w_mat[9]
#
# species_params(params)$w_max[9] <- 450000
#
# species_params(params)$w_max[9]
# species_params(params)$w_mat[9]
# species_params(params)$w_mat25[9]Adjust w_mat values that were changed by default in
newMultispeciesParams
# params_v1 <- params
#
# params_v1@species_params$w_mat[params_v1@species_params$species == "large divers"] <- params_v1@species_params$w_max[params_v1@species_params$species == "large divers"] * 0.9
# params_v1@species_params$w_mat[params_v1@species_params$species == "minke whales"] <- params_v1@species_params$w_max[params_v1@species_params$species == "minke whales"] * 0.9
# params_v1@species_params$w_mat[params_v1@species_params$species == "orca"] <- params_v1@species_params$w_max[params_v1@species_params$species == "orca"] * 0.9
# params_v1@species_params$w_mat[params_v1@species_params$species == "sperm whales"] <- params_v1@species_params$w_max[params_v1@species_params$species == "sperm whales"] * 0.9
#
# params_v1 <- setParams(params_v1)# params_v2 <- params_v1
#
# params_v2@species_params$w_min[params_v2@species_params$species == "small divers"] <- params_v2@species_params$w_mat[params_v2@species_params$species == "small divers"] * 0.85
# params_v2@species_params$w_min[params_v2@species_params$species == "leopard seals"] <- params_v2@species_params$w_mat[params_v2@species_params$species == "leopard seals"] * 0.85
#
# params_v2 <- setParams(params_v2)params <- steady(so_params)
Convergence was achieved in 1.5 years.
Warning: The following species require an unrealistic reproductive efficiency greater than 1: leopard sealsCheck gear params
gear_params(params)Adjust catchability to only select fishing on species with catch data This is a only starting point
gear_params(params)$catchability <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.49, 0, 0, 0.001)Catchability: 1 value Effort changes through time (voyage frequency and length)
gear_params(params)$gear <- c("Main", "Main", "Main", "Main", "Main", "Main",
"Main", "Main", "Main", "Main", "Main", "Main",
"Main", "Main", "Main")
gear_params(params)$l50 <- c(1, 5, 5, 5, 10, 10, 10, 20, 20, 20, 20, 850, 600, 1500, 2200) # values for minke, orca, sperm and baleen are estimated based off `catch_lengths`, while all others are rough guesses, purely as the param won't work with values missing.
gear_params(params)$l25 <- c(0.8, 4, 4, 4, 8, 8, 8, 16, 16, 16, 16, 800, 500, 1400, 1500)
gear_params(params)$sel_func <- c("sigmoid_length", "sigmoid_length", "sigmoid_length", "sigmoid_length", "sigmoid_length", "sigmoid_length",
"sigmoid_length", "sigmoid_length", "sigmoid_length", "sigmoid_length", "sigmoid_length", "sigmoid_length",
"sigmoid_length", "sigmoid_length", "sigmoid_length")
gear_params(params)params_v02 <- setParams(setFishing(params, initial_effort = 0.2))
params_v02 <- steady(params_v02)Convergence was achieved in 52.5 years.
Warning: The following species require an unrealistic reproductive efficiency greater than 1: euphausiids, leopard seals, minke whalessim_v01 <- project(params_v02, t_max = 100)
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plotlyBiomass(sim_v01)sim_v01@params@initial_effortMain
0.2 plotYieldVsSize(sim_v01, species = "baleen whales", catch = catch_lengths,
x_var = "Length")
# plotYieldVsSize(sim_v1, species = "sperm whales", catch = catch_lengths,
# x_var = "Length")
#
# plotYieldVsSize(sim_v1, species = "orca", catch = catch_lengths,
# x_var = "Length")
plotYieldVsSize(sim_v01, species = "minke whales", catch = catch_lengths,
x_var = "Length")
getErrorCustom <- function(vary, params, dat, tol = 0.001,
timetorun = 10)
{
params@species_params$R_max[1:15]<-10^vary[1:15] # R_max for 15 species
params@species_params$erepro[1:15]<-vary[16:30] # erepro for 15 species
params@species_params$interaction_resource[1:15] <- vary[31:45] # interaction_resource for 15 species
params <- setParams(params)
# interaction <- params@interaction
# interaction[] <- matrix(vary[28:108],nrow = 9) # stop at 54 if looking only at 3 biggest species
# params <- setInteraction(params,interaction)
params <- projectToSteady(params, distance_func = distanceSSLogN,
tol = tol, t_max = 200, return_sim = F)
sim <- project(params, t_max = timetorun, progress_bar = F)
sim_biomass = rep(0, length(params@species_params$species))
cutoffLow <- params@species_params$biomass_cutoffLow
if (is.null(cutoffLow))
cutoffLow <- rep(0, no_sp)
cutoffLow[is.na(cutoffLow)] <- 0
cutoffHigh <- params@species_params$biomass_cutoffHigh
if (is.null(cutoffHigh))
cutoffHigh <- rep(0, no_sp)
cutoffHigh[is.na(cutoffHigh)] <- 0
for (j in 1:length(sim_biomass)) {
sim_biomass[j] = sum((sim@n[dim(sim@n)[1],j,] * params@w *
params@dw)[params@w >= cutoffLow[j] & cutoffHigh[j] >= params@w])
}
pred <- log(sim_biomass)
dat <- log(dat)
discrep <- pred - dat
discrep <- (sum(discrep^2))
return(discrep)
}
# create set of params for the optimisation process
tic()
params_optim <- params_v02
vary <- c(log10(params_optim@species_params$R_max),
params_optim@species_params$erepro,
params_optim@species_params$interaction_resource)
params_optim<-setParams(params_optim)
# set up workers
noCores <- parallel::detectCores() - 1 # keep some spare core
cl <- parallel::makeCluster(noCores, setup_timeout = 0.5)
setDefaultCluster(cl = cl)
clusterExport(cl, varlist = "cl",envir=environment())
clusterEvalQ(cl, {
library(mizerExperimental)
library(optimParallel)
})
optim_result <- optimParallel::optimParallel(par=vary,getErrorCustom,params=params_optim,
dat = params_optim@species_params$biomass_observed, method ="L-BFGS-B",
lower=c(rep(-15,15),rep(1e-7,15),rep(.1,15)),
upper= c(rep(15,15),rep(1,15),rep(.99,15)),
parallel=list(loginfo=TRUE, forward=TRUE))
stopCluster(cl)
toc()
saveRDS(optim_result, file="optim_result_v00.RDS")
# optim_result <- readRDS("optim_result_v00.RDS")#put these new vals intospecies_params and go back to the top of this page to re-check the calibration
species_params(params_optim)$R_max<-10^optim_result$par[1:15]
species_params(params_optim)$erepro<-optim_result$par[16:30]
species_params(params_optim)$interaction_resource <-optim_result$par[31:45]
sim_optim <- project(params_optim, t_max = 2000)
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#put these new vals intospecies_params and go back to the top of this page to re-check the calibration
species_params(params_optim)$R_max [1] 4.816091e+00 2.694485e-03 2.079331e-03 1.087521e-04 1.486522e-07 1.727671e-06 6.342514e-07 5.682170e-07 3.435600e-10 1.145037e-08 1.296982e-09
[12] 9.091376e-10 7.838551e-12 3.411213e-11 1.439994e-10species_params(params_optim)$erepro [1] 1.0000000 0.9979727 0.9964291 0.9987793 0.9996089 0.7126939 0.9956963 0.9875731 0.9986068 0.8251681 0.7820067 0.9913709 0.7658567 0.7118458
[15] 0.7590756species_params(params_optim)$interaction_resource [1] 0.9900000 0.9900000 0.9900000 0.9900000 0.9408792 0.9338620 0.9900000 0.9900000 0.9655430 0.9863187 0.9056183 0.9847589 0.9851372 0.9199649
[15] 0.9309803# this function adds a lower boundary to selected size
plotBiomassObservedVsModelCustom <- function (object, species = NULL, ratio = FALSE, log_scale = TRUE,
return_data = FALSE, labels = TRUE, show_unobserved = FALSE)
{
if (is(object, "MizerSim")) {
params = object@params
n <- finalN(object)
}
else if (is(object, "MizerParams")) {
params = object
n <- initialN(params)
}
else {
stop("You have not provided a valid mizerSim or mizerParams object.")
}
sp_params <- params@species_params
species = valid_species_arg(object, species)
if (length(species) == 0)
stop("No species selected, please fix.")
row_select = match(species, sp_params$species)
if (!"biomass_observed" %in% names(sp_params)) {
stop("You have not provided values for the column 'biomass_observed' ",
"in the mizerParams/mizerSim object.")
}
else if (!is.numeric(sp_params$biomass_observed)) {
stop("The column 'biomass_observed' in the mizerParams/mizerSim object",
" is not numeric, please fix.")
}
else {
biomass_observed = sp_params$biomass_observed
}
cutoffLow <- sp_params$biomass_cutoffLow[row_select]
if (is.null(cutoffLow)) {
cutoffLow = rep(0, length(species))
}
else if (!is.numeric(cutoffLow)) {
stop("params@species_params$biomass_cutoffLow is not numeric, \",\n \"please fix.")
}
cutoffLow[is.na(cutoffLow)] <- 0
cutoffHigh <- sp_params$biomass_cutoffHigh[row_select]
if (is.null(cutoffHigh)) {
cutoffHigh = rep(0, length(species))
}
else if (!is.numeric(cutoffHigh)) {
stop("params@species_params$biomass_cutoffHigh is not numeric, \",\n \"please fix.")
}
cutoffHigh[is.na(cutoffHigh)] <- 0
sim_biomass = rep(0, length(species))
for (j in 1:length(species)) {
sim_biomass[j] = sum((n[row_select[j], ] * params@w *
params@dw)[params@w >= cutoffLow[j] & cutoffHigh[j] >= params@w])
}
dummy = data.frame(species = species, model = sim_biomass,
observed = biomass_observed[row_select]) %>% mutate(species = factor(species,
levels = species), is_observed = !is.na(observed) & observed >
0, observed = case_when(is_observed ~ observed, !is_observed ~
model), ratio = model/observed)
if (sum(dummy$is_observed) == 0) {
cat(paste("There are no observed biomasses to compare to model,",
"only plotting model biomasses.", sep = "\n"))
}
if (!show_unobserved) {
dummy <- filter(dummy, is_observed)
}
if (return_data == TRUE)
return(dummy)
tre <- round(sum(abs(1 - dummy$ratio)), digits = 3)
caption <- paste0("Total relative error = ", tre)
if (any(!dummy$is_observed)) {
caption <- paste(caption, "\n Open circles represent species without biomass observation.")
}
if (ratio == FALSE) {
gg <- ggplot(data = dummy, aes(x = observed, y = model,
colour = species, shape = is_observed)) + geom_abline(aes(intercept = 0,
slope = 1), colour = "purple", linetype = "dashed",
size = 1.3) + geom_point(size = 3) + labs(y = "model biomass [g]") +
coord_cartesian(ylim = range(dummy$model, dummy$observed))
}
else {
gg <- ggplot(data = dummy, aes(x = observed, y = ratio,
colour = species, shape = is_observed)) + geom_hline(aes(yintercept = 1),
linetype = "dashed", colour = "purple",
size = 1.3) + geom_point(size = 3) + labs(y = "model biomass / observed biomass") +
coord_cartesian(ylim = range(dummy$ratio))
}
gg <- gg + labs(x = "observed biomass [g]", caption = caption) +
scale_colour_manual(values = getColours(params)[dummy$species]) +
scale_shape_manual(values = c(`TRUE` = 19, `FALSE` = 1)) +
guides(shape = "none")
if (log_scale == TRUE & ratio == FALSE) {
gg = gg + scale_x_log10() + scale_y_log10()
}
if (log_scale == TRUE & ratio == TRUE) {
gg = gg + scale_x_log10()
}
if (labels == TRUE) {
gg = gg + ggrepel::geom_label_repel(aes(label = species),
box.padding = 0.35, point.padding = 0.5, segment.color = "grey50",
show.legend = FALSE, max.overlaps = Inf, seed = 42)
}
gg
}plotBiomassObservedVsModelCustom(sim_optim)
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
Please use `linewidth` instead.
Use tuneParams() to investigate the species with erepro values too high
params_tuned_v01@species_params$erepro
[1] 1.0038283 0.9994687 0.9976619 0.9995862 1.0036193 0.7142939 0.9962189 0.9879108 0.9997113 0.8253208 0.7841239 0.9990174 0.7665787 0.7098369
[15] 0.7553690# create set of params for the optimisation process
tic()
params_optim <- params_tuned_v01
vary <- c(log10(params_optim@species_params$R_max),
params_optim@species_params$erepro,
params_optim@species_params$interaction_resource)
params_optim<-setParams(params_optim)
# set up workers
noCores <- parallel::detectCores() - 1 # keep some spare core
cl <- parallel::makeCluster(noCores, setup_timeout = 0.5)
setDefaultCluster(cl = cl)
clusterExport(cl, varlist = "cl",envir=environment())
clusterEvalQ(cl, {
library(mizerExperimental)
library(optimParallel)
})
optim_result <- optimParallel::optimParallel(par=vary,getErrorCustom,params=params_optim,
dat = params_optim@species_params$biomass_observed, method ="L-BFGS-B",
lower=c(rep(-15,15),rep(1e-7,15),rep(.1,15)),
upper= c(rep(15,15),rep(0.99,15),rep(.99,15)),
parallel=list(loginfo=TRUE, forward=TRUE))
stopCluster(cl)
toc()
saveRDS(optim_result, file="optim_result_v01.RDS")
# optim_result <- readRDS("optim_result_v00.RDS")#put these new vals intospecies_params and go back to the top of this page to re-check the calibration
species_params(params_optim)$R_max<-10^optim_result$par[1:15]
species_params(params_optim)$erepro<-optim_result$par[16:30]
species_params(params_optim)$interaction_resource <-optim_result$par[31:45]
species_params(params_optim)$R_max [1] 4.816945e+00 2.694890e-03 2.077221e-03 1.087269e-04 1.467918e-07 1.735034e-06 6.331959e-07 5.675777e-07 3.507060e-10 1.153278e-08 1.226957e-09
[12] 8.590879e-10 7.806507e-12 2.801063e-11 1.423327e-10species_params(params_optim)$erepro [1] 0.9900000 0.9900000 0.9900000 0.9900000 0.9898627 0.6810777 0.9900000 0.9879108 0.9875105 0.8256844 0.8030409 0.9867368 0.7664345 0.7315449
[15] 0.7560960species_params(params_optim)$interaction_resource [1] 0.9897483 0.9872286 0.9900000 0.9898699 0.9006595 0.8409188 0.9900000 0.9900000 0.9529289 0.9803054 0.7903730 0.9887297 0.9833562 0.6553079
[15] 0.8602099sim_optim_v02 <- project(params_optim, t_max = 1000)
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params_v03 <- steady(params_optim)
Simulation run did not converge after 99 years. Value returned by the distance function was: 0.011566504266491params_loop <- params_v03 |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
matchBiomasses() |> steady() |> matchBiomasses() |> steady()Convergence was achieved in 10.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.
Convergence was achieved in 1.5 years.params_loop@species_params$erepro [1] 0.94874184 0.69740266 0.74789288 0.68164999 0.40986421 0.24796060 0.71872306 0.89462821 0.63623877 0.54381592 0.25947925 0.65810143 0.49211255
[14] 0.12865081 0.07863463sim_v02 <- project(params_loop, t_max = 2000)
Save progress
Try to ween the species of the resource spectra
params_loop@resource_params$kappa
[1] 19.76374params_v04@resource_params$w_pp_cutoff
[1] 10
plotBiomass(sim_v03)
plotBiomassObservedVsModelCustom(sim_v03)
plotBiomassObservedVsModel(sim_v03)
plotDiet(params_v04)
getErrorCustom_v02 <- function(vary, params, dat, tol = 0.001,
timetorun = 10)
{
params@species_params$R_max[1:15]<-10^vary[1:15] # R_max for 15 species
params@species_params$erepro[1:15]<-vary[16:30] # erepro for 15 species
params@species_params$interaction_resource[1:15] <- vary[31:45] # interaction_resource for 15 species
params@resource_params$w_pp_cutoff[1] <- vary[46] # interaction_resource for 15 species
params <- setParams(params)
# interaction <- params@interaction
# interaction[] <- matrix(vary[28:108],nrow = 9) # stop at 54 if looking only at 3 biggest species
# params <- setInteraction(params,interaction)
params <- projectToSteady(params, distance_func = distanceSSLogN,
tol = tol, t_max = 200, return_sim = F)
sim <- project(params, t_max = timetorun, progress_bar = F)
sim_biomass = rep(0, length(params@species_params$species))
cutoffLow <- params@species_params$biomass_cutoffLow
if (is.null(cutoffLow))
cutoffLow <- rep(0, no_sp)
cutoffLow[is.na(cutoffLow)] <- 0
cutoffHigh <- params@species_params$biomass_cutoffHigh
if (is.null(cutoffHigh))
cutoffHigh <- rep(0, no_sp)
cutoffHigh[is.na(cutoffHigh)] <- 0
for (j in 1:length(sim_biomass)) {
sim_biomass[j] = sum((sim@n[dim(sim@n)[1],j,] * params@w *
params@dw)[params@w >= cutoffLow[j] & cutoffHigh[j] >= params@w])
}
pred <- log(sim_biomass)
dat <- log(dat)
discrep <- pred - dat
discrep <- (sum(discrep^2))
return(discrep)
}
# create set of params for the optimisation process
tic()
params_optim <- params_v04
vary <- c(log10(params_optim@species_params$R_max),
params_optim@species_params$erepro,
params_optim@species_params$interaction_resource,
params_optim@resource_params$w_pp_cutoff)
params_optim<-setParams(params_optim)
# set up workers
noCores <- parallel::detectCores() - 1 # keep some spare core
cl <- parallel::makeCluster(noCores, setup_timeout = 0.5)
setDefaultCluster(cl = cl)
clusterExport(cl, varlist = "cl",envir=environment())
clusterEvalQ(cl, {
library(mizerExperimental)
library(optimParallel)
})
optim_result <- optimParallel::optimParallel(par=vary,getErrorCustom_v02,params=params_optim,
dat = params_optim@species_params$biomass_observed, method ="L-BFGS-B",
lower=c(rep(-15,15),rep(1e-7,15),rep(.1,15), 0.1),
upper= c(rep(15,15),rep(0.99,15),rep(.99,15),1000),
parallel=list(loginfo=TRUE, forward=TRUE))
stopCluster(cl)
toc()
saveRDS(optim_result, file="optim_result_v02.RDS")
# optim_result <- readRDS("optim_result_v00.RDS")#put these new vals intospecies_params and go back to the top of this page to re-check the calibration
species_params(params_optim)$R_max<-10^optim_result$par[1:15]
species_params(params_optim)$erepro<-optim_result$par[16:30]
species_params(params_optim)$interaction_resource <-optim_result$par[31:45]
resource_params(params_optim)$w_pp_cutoff <- optim_result$par[46]
species_params(params_optim)$R_max [1] 4.824845e+00 2.704878e-03 2.078290e-03 1.087379e-04 1.452868e-07 2.373280e-06 6.334551e-07 5.664204e-07
[9] 4.012486e-10 1.147254e-08 1.282038e-09 1.001733e-09 8.019699e-12 2.460401e-11 1.411257e-10species_params(params_optim)$erepro [1] 0.9487424 0.6974029 0.7478930 0.6816501 0.4110420 0.3877134 0.7187231 0.8946282 0.7424363 0.5446118 0.4133560
[12] 0.7679180 0.4922805 0.4796008 0.1651285species_params(params_optim)$interaction_resource [1] 0.9880931 0.9748302 0.9900000 0.9900000 0.8636127 0.8778679 0.9900000 0.9900000 0.9751581 0.9745590 0.7326587
[12] 0.9900000 0.9821797 0.4116474 0.7857996sim_v04 <- project(params_optim, t_max = 1000)
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plotBiomassObservedVsModelCustom(sim_v04)
plotBiomassObservedVsModel(sim_v04)
plotDiet(params_optim)
params_tuned_v02@species_params$erepro
[1] 0.9485557 0.6971840 0.7477216 0.6816138 0.4110374 0.3877374 0.7186214 0.8940243 0.7432162 0.5455491 0.4207180
[12] 0.7685176 0.4934246 0.4891521 0.1642704species w_min w_mat w_max beta k_vb h min_depth max_depth water.column.use p_time_prydz pc_annual_offspring 5 salps 3.162278e-05 0.2511886 25.11886 1000 NA 33 0 500 non DVM 1 NA biomass_observed 5 0.652
params_v05@species_params$erepro
[1] 9.485557e-01 6.971840e-01 7.477216e-01 6.816138e-01 4.110374e-01 3.877374e-01 7.186214e-01 8.940243e-01
[9] 7.432162e-01 5.455491e-01 4.207180e-01 7.685176e-01 4.934246e-01 4.891521e-01 1.642704e-01 1.706412e-06
params_v05@interaction
prey
predator euphausiids mesopelagic fishes bathypelagic fishes shelf and coastal fishes flying birds
euphausiids 1.0000000 0.1250 0.1250 0.137362637 0.050000000
mesopelagic fishes 0.1250000 1.0000 1.0000 0.072800000 0.012500000
bathypelagic fishes 0.1250000 1.0000 1.0000 0.072800000 0.012500000
shelf and coastal fishes 0.1373626 0.0728 0.0728 1.000000000 0.006868132
flying birds 0.0500000 0.0125 0.0125 0.006868132 0.000000000
small divers 0.1000000 0.0250 0.0250 0.013736264 0.000000000
squids 0.2500000 0.5000 0.5000 0.072800000 0.012500000
toothfishes 0.2500000 0.5000 0.5000 0.072800000 0.012500000
leopard seals 0.1000000 0.0250 0.0250 0.013736264 0.000000000
medium divers 0.1666667 0.3750 0.3750 0.048533333 0.000000000
large divers 0.1666667 0.3750 0.3750 0.048533333 0.000000000
minke whales 0.0500000 0.0125 0.0125 0.006868132 0.000000000
orca 0.2000000 0.0500 0.0500 0.027472527 0.000000000
sperm whales 0.1250000 0.5000 0.5000 0.036400000 0.000000000
baleen whales 0.2000000 0.0500 0.0500 0.027472527 0.000000000
salps 1.0000000 1.0000 1.0000 1.000000000 1.000000000
prey
predator small divers squids toothfishes leopard seals medium divers large divers minke whales
euphausiids 0.10000000 0.2500 0.2500 0.10000000 0.16666667 0.16666667 0.050000000
mesopelagic fishes 0.02500000 0.5000 0.5000 0.02500000 0.37500000 0.37500000 0.012500000
bathypelagic fishes 0.02500000 0.5000 0.5000 0.02500000 0.37500000 0.37500000 0.012500000
shelf and coastal fishes 0.01373626 0.0728 0.0728 0.01373626 0.04853333 0.04853333 0.006868132
flying birds 0.00000000 0.0125 0.0125 0.00000000 0.00000000 0.00000000 0.000000000
small divers 0.00000000 0.0250 0.0250 1.00000000 0.00000000 0.00000000 0.000000000
squids 0.02500000 1.0000 1.0000 0.02500000 0.37500000 0.37500000 0.012500000
toothfishes 0.02500000 1.0000 1.0000 0.02500000 0.37500000 0.37500000 0.012500000
leopard seals 1.00000000 0.0250 0.0250 1.00000000 0.26666667 0.26666667 1.000000000
medium divers 0.00000000 0.3750 0.3750 0.26666667 0.00000000 0.00000000 0.000000000
large divers 0.00000000 0.3750 0.3750 0.26666667 0.00000000 0.00000000 0.000000000
minke whales 0.00000000 0.0125 0.0125 1.00000000 0.00000000 0.00000000 0.000000000
orca 1.00000000 0.0500 0.0500 1.00000000 0.53333333 0.53333333 1.000000000
sperm whales 0.00000000 0.5000 0.5000 0.20000000 0.00000000 0.00000000 0.000000000
baleen whales 0.00000000 0.0500 0.0500 1.00000000 0.00000000 0.00000000 0.000000000
salps 1.00000000 1.0000 1.0000 1.00000000 1.00000000 1.00000000 1.000000000
prey
predator orca sperm whales baleen whales salps
euphausiids 0.20000000 0.1250 0.20000000 1
mesopelagic fishes 0.05000000 0.5000 0.05000000 1
bathypelagic fishes 0.05000000 0.5000 0.05000000 1
shelf and coastal fishes 0.02747253 0.0364 0.02747253 1
flying birds 0.00000000 0.0000 0.00000000 1
small divers 1.00000000 0.0000 0.00000000 1
squids 0.05000000 0.5000 0.05000000 1
toothfishes 0.05000000 0.5000 0.05000000 1
leopard seals 1.00000000 0.2000 1.00000000 1
medium divers 0.53333333 0.0000 0.00000000 1
large divers 0.53333333 0.0000 0.00000000 1
minke whales 1.00000000 0.0000 0.00000000 1
orca 0.00000000 0.4000 1.00000000 1
sperm whales 0.40000000 0.0000 0.00000000 1
baleen whales 1.00000000 0.0000 0.00000000 1
salps 1.00000000 1.0000 1.00000000 1so_theta[16,] # automated salps interaction when added using addSpecies()
so_theta[16,] <- salp_row
so_theta[,16] # automated salps interaction when added using addSpecies()
so_theta[,16] <- salp_row
so_thetaparams_v06 <- setParams(params_v05, interaction = so_theta)
params_v06@interaction
initialN(params_v06) <- sim_v07@n[dim(sim_v07@n)[1],,]sim_v08 <- project(params_v06, t_max = 100)
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plotDiet(params_v06)
Fill in missing and adjust beta values for zooplankton groups using Heneghan et al. 2020 (10.1016/j.ecolmodel.2020.109265)
Need a value for microzooplankton microzooplankton (in McCormack et al. 2020) is composed of Heterotrophic dinoflagellates, tintinnids, ciliates, copepod nauplii Heneghan et al. log10PPMR values for: Hetero.Flagellates = 0.2–0.72 -> 0.46 Hetero.Ciliates = 2.5–2.9 -> 2.7 Mean: (2.7+0.46)/2 = 1.58 10^1.58 = 38.01894
Need to adjust values for mesozoo, other macrozoo, euphausiids, salps
Heneghan et al. log10PPMR values (midpoints for range) for: salps = 6.8–11.7 -> 9.25 10^9.25 = 1778279410
euphausiids = 6.6–7.8 -> 7.2 10^7.2 = 15848932
mesozoo (copepods) Omni.Cop. = 3.6–4.6 -> 4.1 Carn.Cop. = 0.8–1.9 -> 1.35 Mean: (4.1+1.35)/2 = 2.725 10^2.725 = 530.8844
other macrozoo () Chaetognaths = 1.9–3.4 -> 2.65 10^2.65 = 446.6836
params_tuned_v03 <- tuneParams(params_v06)
params_tuned_v04 <- tuneParams(params_tuned_v03) # updated salps PPMR to match empirical estimates from Heneghan et al 2020getErrorCustom_v3 <- function(vary, params, dat, tol = 0.001,
timetorun = 10)
{
params@species_params$R_max[1:16]<-10^vary[1:16] # R_max for 15 species
params@species_params$erepro[1:16]<-vary[17:32] # erepro for 15 species
params@species_params$interaction_resource[1:16] <- vary[33:48] # interaction_resource for 15 species
params <- setParams(params)
# interaction <- params@interaction
# interaction[] <- matrix(vary[28:108],nrow = 9) # stop at 54 if looking only at 3 biggest species
# params <- setInteraction(params,interaction)
params <- projectToSteady(params, distance_func = distanceSSLogN,
tol = tol, t_max = 200, return_sim = F)
sim <- project(params, t_max = timetorun, progress_bar = F)
sim_biomass = rep(0, length(params@species_params$species))
cutoffLow <- params@species_params$biomass_cutoffLow
if (is.null(cutoffLow))
cutoffLow <- rep(0, no_sp)
cutoffLow[is.na(cutoffLow)] <- 0
cutoffHigh <- params@species_params$biomass_cutoffHigh
if (is.null(cutoffHigh))
cutoffHigh <- rep(0, no_sp)
cutoffHigh[is.na(cutoffHigh)] <- 0
for (j in 1:length(sim_biomass)) {
sim_biomass[j] = sum((sim@n[dim(sim@n)[1],j,] * params@w *
params@dw)[params@w >= cutoffLow[j] & cutoffHigh[j] >= params@w])
}
pred <- log(sim_biomass)
dat <- log(dat)
discrep <- pred - dat
discrep <- (sum(discrep^2))
return(discrep)
}# create set of params for the optimisation process
tic()
params_optim <- params_tuned_v04
vary <- c(log10(params_optim@species_params$R_max),
params_optim@species_params$erepro,
params_optim@species_params$interaction_resource)
params_optim<-setParams(params_optim)
# set up workers
noCores <- parallel::detectCores() - 1 # keep some spare core
cl <- parallel::makeCluster(noCores, setup_timeout = 0.5)
setDefaultCluster(cl = cl)
clusterExport(cl, varlist = "cl",envir=environment())
clusterEvalQ(cl, {
library(mizerExperimental)
library(optimParallel)
})
optim_result <- optimParallel::optimParallel(par=vary,getErrorCustom_v3,params=params_optim,
dat = params_optim@species_params$biomass_observed, method ="L-BFGS-B",
lower=c(rep(-15,16),rep(1e-7,16),rep(.1,16)),
upper= c(rep(15,16),rep(0.99,16),rep(.99,16)),
parallel=list(loginfo=TRUE, forward=TRUE))
stopCluster(cl)
toc()
saveRDS(optim_result, file="optim_result_v03.RDS")
# optim_result <- readRDS("optim_result_v00.RDS")#put these new vals intospecies_params and go back to the top of this page to re-check the calibration
species_params(params_optim)$R_max<-10^optim_result$par[1:16]
species_params(params_optim)$erepro<-optim_result$par[17:32]
species_params(params_optim)$interaction_resource <-optim_result$par[33:48]
species_params(params_optim)$R_max [1] 4.830654e+00 2.852640e-03 2.269715e-03 1.109938e-04 1.434663e-07 2.904891e-06 7.470823e-07 6.594487e-07
[9] 3.846612e-10 1.148052e-08 1.182556e-09 1.143978e-09 7.843004e-12 2.415528e-11 1.420689e-10 2.223152e-02species_params(params_optim)$erepro [1] 0.9485563 0.6971843 0.7477218 0.6816140 0.4121015 0.3240383 0.7186214 0.8940243 0.7998257 0.5462956 0.5012431
[12] 0.8250529 0.4934160 0.7479503 0.1740849 0.5808559species_params(params_optim)$interaction_resource [1] 0.9867241 0.9633324 0.9838021 0.9853247 0.8239974 0.8550857 0.9879850 0.9871328 0.9820318 0.9728534 0.6259341
[12] 0.9829386 0.9789605 0.3306710 0.7138859 0.9896762sim_v09 <- project(params_optim, t_max = 200)
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params_v07 <- params_optim
initialN(params_v07) <- sim_v09@n[dim(sim_v09@n)[1],,]sim_v10 <- project(params_v07, t_max = 200)
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plotDiet(params_v07)
params_tuned_v05@species_params$erepro
[1] 0.9482281 0.6939526 0.7420393 0.6801803 0.4120497 0.3239043 0.7167263 0.8926139 0.7990909 0.5454373 0.4995779
[12] 0.8216917 0.4943938 0.7415872 0.1725889 0.5806101# saveRDS(params_tuned_v05, "params_optim_v02.rds")
params_tuned_v05 <- readRDS("params_optim_v02.rds")sim_v12 <- project(params_tuned_v05, t_max = 500)
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sim_v13 <- project(params_tuned_v05, t_max = 500)
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plotBiomassObservedVsModelCustom(sim_v13)
Gradually reducing resource maximum size in tuneParams(), as doing it directly in the setParams() route will tell you the w_pp_cutoff has changed, but it will still incorporate a background resource up to the w_pp_cutoff that was originally used in newMultispeciesParams()
params_tuned_v06@species_params$R_max
[1] 4.664858e+00 2.181613e-03 1.899593e-03 7.871131e-05 6.543008e-08 4.569620e-07 4.394343e-07
[8] 7.671631e-07 1.613946e-10 1.375584e-08 Inf 7.111924e-10 1.654097e-11 1.156828e-12
[15] 9.964411e-12 1.183440e-02params_tuned_v10@resource_params$w_pp_cutoff
[1] 100# create set of params for the optimisation process
tic()
params_optim <- params_tuned_v10
vary <- c(log10(params_optim@species_params$R_max),
params_optim@species_params$erepro,
params_optim@species_params$interaction_resource)
params_optim<-setParams(params_optim)
# set up workers
noCores <- parallel::detectCores() - 1 # keep some spare core
cl <- parallel::makeCluster(noCores, setup_timeout = 0.5)
setDefaultCluster(cl = cl)
clusterExport(cl, varlist = "cl",envir=environment())
clusterEvalQ(cl, {
library(mizerExperimental)
library(optimParallel)
})
optim_result <- optimParallel::optimParallel(par=vary,getErrorCustom_v3,params=params_optim,
dat = params_optim@species_params$biomass_observed, method ="L-BFGS-B",
lower=c(rep(-15,16),rep(1e-7,16),rep(.1,16)),
upper= c(rep(15,16),rep(0.99,16),rep(.99,16)),
parallel=list(loginfo=TRUE, forward=TRUE))
stopCluster(cl)
toc()
saveRDS(optim_result, file="optim_result_v04.RDS")
# optim_result <- readRDS("optim_result_v04.RDS")#put these new vals intospecies_params and go back to the top of this page to re-check the calibration
species_params(params_optim)$R_max<-10^optim_result$par[1:16]
species_params(params_optim)$erepro<-optim_result$par[17:32]
species_params(params_optim)$interaction_resource <-optim_result$par[33:48]
species_params(params_optim)$R_max [1] 4.455921e+00 2.984966e-03 2.445172e-03 1.104837e-04 1.274650e-07 2.220551e-06 7.643668e-07
[8] 9.188736e-07 3.643475e-10 1.265181e-08 1.011248e-09 1.083077e-09 8.519112e-12 1.730661e-11
[15] 1.425133e-10 2.233647e-02species_params(params_optim)$erepro [1] 0.9485960 0.6957507 0.7442478 0.6805366 0.4135304 0.4176807 0.7177938 0.8906210 0.8206870 0.5417358
[11] 0.8990635 0.8349824 0.4713801 0.7878068 0.1844293 0.5808197species_params(params_optim)$interaction_resource [1] 0.9900000 0.9757103 0.9900000 0.9898909 0.7710204 0.8767302 0.9900000 0.9900000 0.9532022 0.9867254
[11] 0.6868874 0.9900000 0.9787623 0.3054025 0.6562795 0.9897251sim_v14 <- project(params_optim, t_max = 2000)
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params_v08 <- params_optim
initialN(params_v08) <- sim_v14@n[dim(sim_v14@n)[1],,]params_tuned_v07@species_params$erepro
[1] 0.9485821 0.6956672 0.7441494 0.6805253 0.4135289 0.4176799 0.7177760 0.8905317 0.8214728 0.5421544
[11] 0.9005723 0.8378706 0.4727959 0.7956823 0.1849946 0.5808061params_tuned_v07@species_params$R_max [1] 4.289003e+00 2.077178e-03 1.798165e-03 7.486774e-05 6.487583e-08 3.340159e-07 4.677235e-07
[8] 5.228974e-07 1.661754e-10 7.504264e-09 3.456007e-10 6.683396e-10 7.048537e-12 3.203121e-13
[15] 5.664588e-12 1.181176e-02sim_v15 <- project(params_tuned_v07, t_max = 2000)
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plotDiet(params_tuned_v07)
sim_v17 <- project(params_loop, t_max = 500)
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[===========================================>] 100% ETA: 0splotlyBiomass(sim_v17)plotBiomassObservedVsModel(sim_v17)
plotBiomassObservedVsModelCustom(sim_v17)
plotBiomassRelative(sim_v17)
plotDiet(params_loop)
sim_v18 <- project(params_loop, t_max = 1000)
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[===========================================>] 100% ETA: 0splotlyBiomass(sim_v18)plotBiomassObservedVsModel(sim_v18)
plotBiomassObservedVsModelCustom(sim_v18)
plotBiomassRelative(sim_v18)
plotDiet(params_loop)
---
title: "R Notebook"
output: html_notebook
---

Step 1: revised catch for 2010-2020
Step 2: tune resilience of all the species (i.e., long-lived, slower growing species = vulnerable...etc...)
Step 3: calibrated model with fishing mortality represented (time-averaged catch comparison, model vs observed) = base model
Step 4: check base model without fishing to ensure co-existence is still present, observed biomass would not be specified
Step 5: Experiments through time - effort functions - uses the base model with no fishing effort as a starting point
Step 6: Expose the base model to fishing through time and compare to observed catch time series 
 - start from 0 effort at first time-step to max effort approx around collapse
 - If the modelled versus observed don't match, need to adjust effort/or initial biological params and experiment to see what improves
 - hand tune / optim options
Adjust and recalibrate if needed (optim)
Use rules to constrain - i.e., 
- time-averaged biomasses within calibration period must be +- xx %
- can optim things like fishing-size selectivity, PPMRs etc


Can we reproduce the time series?


Load libraries 
```{r}
remotes::install_github("sizespectrum/mizerExperimental")
library(mizerExperimental)
# remotes::install_github("sizespectrum/mizerMR")
# library(mizerMR)
# library(mizer)
library(tidyverse)
library(tictoc)
library(parallel)
# library(plotly)
```

Catch data for all indivudals and summarised as totals per species each year

Maybe this isn't expressing effort, rather fishing mortality


```{r}
# df_ind_CPUE <- readRDS("ind_catch_weight_BanzareBank_1930_2019_CPUE.rds")
# df_CPUE_kg_day <- readRDS("catch_timeseries_BanzareBank_1930_2019_CPUE.rds")
# 
# glimpse(df_ind_CPUE)
# glimpse(df_CPUE_kg_day)
```

Yield for the period that matches Ecopath model, post 2000 annual average
```{r}
# df_yield_2000 <- df_CPUE_kg_day %>% 
#   filter(Year > 2009) %>% 
#   group_by(Species) %>% 
#   summarise(yield = sum(total_catch_kg)/10)
# 
# df_yield_2000
```



effort_days/max value plotted by species
This might indicate the effort curve required, especially if there is a difference between different species
```{r}
# df_plot <- df_CPUE_kg_day %>% 
#   filter(Year > 2000) %>% 
#   group_by(Species) %>% 
#   mutate(max_effort = max(effort_days)) %>% 
#   group_by(Species,Year) %>% 
#   mutate(effort_standard = effort_days/max_effort)
```

Relative change in catch pre-1930, could inform the relative change in effort
Ask Cami, how could I reconstruct the effort to the initial point of catch when effort = 0...i.e., approx 1850 
```{r}
# df_plot %>% 
#   ggplot(aes(x = Year, y = effort_standard, colour = Species)) +
#   geom_smooth() +
#   geom_line()
#   # facet_wrap(~Species) 
```

```{r}
# df_plot %>% 
#   ggplot(aes(x = Year, y = CPUE, colour = Species)) +
#   # geom_smooth() +
#   geom_line()
#   # facet_wrap(~Species) 
```

Load catch length
```{r}
catch_lengths <- readRDS("catch_lengths.rds")

glimpse(catch_lengths)
```
The value for euphausiids `obs_yield` is from  `calibration_catch_histsoc_1850_2004_regional_models.csv` found at http://portal.sf.utas.edu.au/thredds/catalog/gem/fishmip/ISIMIP3a/InputData/fishing/histsoc/catalog.html. It is the annual average yield over a 22 year period for the Prydz Bay Region.

The values of `obs_yield` for minke whales (2175476136), orca (19923729), sperm whales (4062177445), and baleen whales (50341696055) are the annual average yield from 1930 - 2019 from IWC records of whaling in the Prydz Bay model domain (1,433,028 km2).

Adjust the catch to represent the 2010 - 2020 time period + incorporate toothfish catch if possible (i.e., Stacey's paper values)




```{r}
df_ind_CPUE <- readRDS("ind_catch_weight_BanzareBank_1930_2019_CPUE.rds")
df_CPUE_kg_day <- readRDS("catch_timeseries_BanzareBank_1930_2019_CPUE.rds")

glimpse(df_ind_CPUE)
glimpse(df_CPUE_kg_day)
```

Yield for the period that matches Ecopath model, post 2000 annual average
```{r}
df_yield_2000_2019 <- df_CPUE_kg_day %>% 
  filter(Year > 2000) %>% 
  group_by(Species) %>% 
  summarise(yield = sum(total_catch_kg)/19)

df_yield_2000_2019
```

Add yield in tonnes per km2, same as g m2 and it is consistent with `biomass_observed` 

1.474341e+12 m^2 for model domain

1.474341e+12/1e+6 = 1474341 km^2

507511 kg of minke whale per year from 2000 - 2019

507.5/1474341 = 0.0003442216 tonnes Minke whale yield per km2 from 2000 - 2019

15619.24 kg of baleen whale per year from 2000 - 2019

15.6/1474341 = 1.0581e-05 tonnes of baleen whale per km2 from 2000 - 2019

```{r}
obs_yield <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0003442216, 0, 0, 1.0581e-05) # to add a yield_observed column in species_params

```


```{r}
biomass_cutoff <- c(0.1, 1, 1, 1, 40, 4500, 1, 1, 200000, 10000, 135000, 600000, 490000, 3650000, 2250000) # to add a biomass_cutoff column in species_params

```


Steady-state model with no fishing effort calibrated to observed biomass values from McCormack et al. 2020 that represent an average state of the food web from 2010-2020.

```{r}
# params <-  readRDS("stage1_steady_vXX.rds") # Incorrect orca and leopard seal w_max values
so_params <- readRDS("params_16_March_2023.rds") # Updated w_max for orca and leopard seals

# params <- setParams(setFishing(params, initial_effort = 0.2))

```


```{r}
# species_params(params)$yield_observed <- obs_yield
# species_params(params)$biomass_cutoff <- biomass_cutoff
# species_params(params)$biomass_cutoffLow <- biomass_cutoff
# species_params(params)$biomass_cutoffHigh <- species_params(params)$w_max

species_params(so_params)$yield_observed <- obs_yield
species_params(so_params)$biomass_cutoff <- biomass_cutoff
species_params(so_params)$biomass_cutoffLow <- biomass_cutoff
species_params(so_params)$biomass_cutoffHigh <- species_params(so_params)$w_max

# species_params(params) |> dplyr::select(species, yield_observed, w_mat, w_max, R_max)
species_params(so_params) |> dplyr::select(species, yield_observed, w_mat, w_max, R_max)
```



```{r}
# groups |> dplyr::select(species, yield_observed, biomass_observed, biomass_cutoff, w_min)
```


```{r}
# yield_observed <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0003442216, 0, 0, 1.0581e-05) # to add a yield_observed column in species_params
# 
# species_params(params)$yield_observed <- yield_observed
```

```{r}
# biomass_cutoff <- c(0.1, 1, 1, 1, 40, 4500, 1, 1, 200000, 10000, 135000, 600000, 490000, 3650000, 2250000) # to add a biomass_cutoff column in species_params
# 
# species_params(params)$biomass_cutoff <- biomass_cutoff
```

Update max and mat size for orca using sealifebase values rather than the previous w_max of 6t
```{r}
# species_params(params)$species[13]
# species_params(params)$w_max[13]
# species_params(params)$w_mat[13]
# 
# species_params(params)$w_max[13] <- 10628034
# 
# species_params(params)$w_mat[13] <- 3198855
# 
# species_params(params)$w_max[13]
# species_params(params)$w_mat[13]
# species_params(params)$w_mat25[13]
```


Update max and mat size for leopard seal using sealifebase values. Max weight observed reported as 450kg. Using the sealifebase values for L-W conversion and max length results in an estimated max weight of 545875.2g, which is too high above the max weight observed.
```{r}
# species_params(params)$species[9]
# species_params(params)$w_max[9]
# species_params(params)$w_mat[9]
# 
# species_params(params)$w_max[9] <- 450000
#  
# species_params(params)$w_max[9]
# species_params(params)$w_mat[9]
# species_params(params)$w_mat25[9]
```

Adjust `w_mat` values that were changed by default in `newMultispeciesParams`
```{r}
# params_v1 <- params
# 
# params_v1@species_params$w_mat[params_v1@species_params$species == "large divers"]  <- params_v1@species_params$w_max[params_v1@species_params$species == "large divers"] * 0.9
# params_v1@species_params$w_mat[params_v1@species_params$species == "minke whales"]  <- params_v1@species_params$w_max[params_v1@species_params$species == "minke whales"] * 0.9
# params_v1@species_params$w_mat[params_v1@species_params$species == "orca"]  <- params_v1@species_params$w_max[params_v1@species_params$species == "orca"] * 0.9
# params_v1@species_params$w_mat[params_v1@species_params$species == "sperm whales"]  <- params_v1@species_params$w_max[params_v1@species_params$species == "sperm whales"] * 0.9
# 
# params_v1 <- setParams(params_v1)
```


```{r}
# params_v2 <- params_v1
# 
# params_v2@species_params$w_min[params_v2@species_params$species == "small divers"]  <- params_v2@species_params$w_mat[params_v2@species_params$species == "small divers"] * 0.85
# params_v2@species_params$w_min[params_v2@species_params$species == "leopard seals"]  <- params_v2@species_params$w_mat[params_v2@species_params$species == "leopard seals"] * 0.85
# 
# params_v2 <- setParams(params_v2)
```


```{r}
params <- steady(so_params)
```

Check gear params

```{r}
gear_params(params)
```

Adjust catchability to only select fishing on species with catch data
This is a only starting point
```{r}
gear_params(params)$catchability <-  c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.49, 0, 0, 0.001)
```


Catchability: 1 value 
Effort changes through time (voyage frequency and length)

```{r}
gear_params(params)$gear <-  c("Main", "Main", "Main", "Main", "Main", "Main",
                                   "Main", "Main", "Main", "Main", "Main", "Main",
                                   "Main", "Main", "Main")

gear_params(params)$l50 <-  c(1, 5, 5, 5, 10, 10, 10, 20, 20, 20, 20, 850, 600, 1500, 2200) # values for minke, orca, sperm and baleen are estimated based off `catch_lengths`, while all others are rough guesses, purely as the param won't work with values missing.

gear_params(params)$l25 <-  c(0.8, 4, 4, 4, 8, 8, 8, 16, 16, 16, 16, 800, 500, 1400, 1500)

gear_params(params)$sel_func <-  c("sigmoid_length", "sigmoid_length", "sigmoid_length", "sigmoid_length", "sigmoid_length", "sigmoid_length",
                                   "sigmoid_length", "sigmoid_length", "sigmoid_length", "sigmoid_length", "sigmoid_length", "sigmoid_length",
                                   "sigmoid_length", "sigmoid_length", "sigmoid_length")

gear_params(params)
```


```{r}
params_v02 <- setParams(setFishing(params, initial_effort = 0.2))

params_v02 <- steady(params_v02)

sim_v01 <- project(params_v02, t_max = 100)
plot(sim_v01)
plotlyBiomass(sim_v01)
sim_v01@params@initial_effort
```


```{r}
plotYieldVsSize(sim_v01, species = "baleen whales", catch = catch_lengths, 
                x_var = "Length")

# plotYieldVsSize(sim_v1, species = "sperm whales", catch = catch_lengths, 
#                 x_var = "Length")
# 
# plotYieldVsSize(sim_v1, species = "orca", catch = catch_lengths, 
#                 x_var = "Length")

plotYieldVsSize(sim_v01, species = "minke whales", catch = catch_lengths, 
                x_var = "Length")
```

```{r}
getErrorCustom <- function(vary, params, dat, tol = 0.001, 
    timetorun = 10)
{
  params@species_params$R_max[1:15]<-10^vary[1:15] # R_max for 15 species
  params@species_params$erepro[1:15]<-vary[16:30] # erepro for 15 species
  params@species_params$interaction_resource[1:15] <- vary[31:45] # interaction_resource for 15 species
  params <- setParams(params)
  # interaction <- params@interaction
  # interaction[] <- matrix(vary[28:108],nrow = 9) # stop at 54 if looking only at 3 biggest species
  
  # params <- setInteraction(params,interaction)
    params <- projectToSteady(params, distance_func = distanceSSLogN, 
        tol = tol, t_max = 200, return_sim = F)
    
    sim <- project(params, t_max = timetorun, progress_bar = F)
    
    sim_biomass = rep(0, length(params@species_params$species))
    
        cutoffLow <- params@species_params$biomass_cutoffLow
    if (is.null(cutoffLow)) 
        cutoffLow <- rep(0, no_sp)
    cutoffLow[is.na(cutoffLow)] <- 0
    
        cutoffHigh <- params@species_params$biomass_cutoffHigh
    if (is.null(cutoffHigh)) 
        cutoffHigh <- rep(0, no_sp)
    cutoffHigh[is.na(cutoffHigh)] <- 0
        
    for (j in 1:length(sim_biomass)) {
        sim_biomass[j] = sum((sim@n[dim(sim@n)[1],j,] * params@w * 
            params@dw)[params@w >= cutoffLow[j] & cutoffHigh[j] >= params@w])
    }
    
     pred <- log(sim_biomass)
    dat <- log(dat)
    discrep <- pred - dat
    discrep <- (sum(discrep^2))
    return(discrep)
}
    
```


```{r}
# create set of params for the optimisation process
tic()

params_optim <- params_v02

vary <- c(log10(params_optim@species_params$R_max),
          params_optim@species_params$erepro,
          params_optim@species_params$interaction_resource)

params_optim<-setParams(params_optim)

# set up workers
noCores <- parallel::detectCores() - 1 # keep some spare core
cl <- parallel::makeCluster(noCores, setup_timeout = 0.5)
setDefaultCluster(cl = cl)
clusterExport(cl, varlist = "cl",envir=environment())
clusterEvalQ(cl, {
  library(mizerExperimental)
  library(optimParallel)
})

optim_result <- optimParallel::optimParallel(par=vary,getErrorCustom,params=params_optim, 
                                             dat = params_optim@species_params$biomass_observed, method ="L-BFGS-B", 
                                             lower=c(rep(-15,15),rep(1e-7,15),rep(.1,15)),
                                             upper= c(rep(15,15),rep(1,15),rep(.99,15)),
                                             parallel=list(loginfo=TRUE, forward=TRUE))
stopCluster(cl)

toc()

saveRDS(optim_result, file="optim_result_v00.RDS")
# optim_result <- readRDS("optim_result_v00.RDS")
```


```{r}
#put these new vals intospecies_params and go back to the top of this page to re-check the calibration 
species_params(params_optim)$R_max<-10^optim_result$par[1:15]
species_params(params_optim)$erepro<-optim_result$par[16:30]
species_params(params_optim)$interaction_resource <-optim_result$par[31:45]
 
sim_optim <- project(params_optim, t_max = 2000)
plotBiomass(sim_optim)
```

```{r}
#put these new vals intospecies_params and go back to the top of this page to re-check the calibration 
species_params(params_optim)$R_max
species_params(params_optim)$erepro
species_params(params_optim)$interaction_resource
```


```{r}
# this function adds a lower boundary to selected size
plotBiomassObservedVsModelCustom <- function (object, species = NULL, ratio = FALSE, log_scale = TRUE, 
    return_data = FALSE, labels = TRUE, show_unobserved = FALSE) 
{
    if (is(object, "MizerSim")) {
        params = object@params
        n <- finalN(object)
    }
    else if (is(object, "MizerParams")) {
        params = object
        n <- initialN(params)
    }
    else {
        stop("You have not provided a valid mizerSim or mizerParams object.")
    }
    sp_params <- params@species_params
    species = valid_species_arg(object, species)
    if (length(species) == 0) 
        stop("No species selected, please fix.")
    row_select = match(species, sp_params$species)
    if (!"biomass_observed" %in% names(sp_params)) {
        stop("You have not provided values for the column 'biomass_observed' ", 
            "in the mizerParams/mizerSim object.")
    }
    else if (!is.numeric(sp_params$biomass_observed)) {
        stop("The column 'biomass_observed' in the mizerParams/mizerSim object", 
            " is not numeric, please fix.")
    }
    else {
        biomass_observed = sp_params$biomass_observed
    }
    
    cutoffLow <- sp_params$biomass_cutoffLow[row_select]
    if (is.null(cutoffLow)) {
        cutoffLow = rep(0, length(species))
    }
    else if (!is.numeric(cutoffLow)) {
        stop("params@species_params$biomass_cutoffLow is not numeric, \",\n                 \"please fix.")
    }
    cutoffLow[is.na(cutoffLow)] <- 0
    
    cutoffHigh <- sp_params$biomass_cutoffHigh[row_select]
    if (is.null(cutoffHigh)) {
        cutoffHigh = rep(0, length(species))
    }
    else if (!is.numeric(cutoffHigh)) {
        stop("params@species_params$biomass_cutoffHigh is not numeric, \",\n                 \"please fix.")
    }
    cutoffHigh[is.na(cutoffHigh)] <- 0
    
    sim_biomass = rep(0, length(species))
    for (j in 1:length(species)) {
        sim_biomass[j] = sum((n[row_select[j], ] * params@w * 
            params@dw)[params@w >= cutoffLow[j] & cutoffHigh[j] >= params@w])
    }
    dummy = data.frame(species = species, model = sim_biomass, 
        observed = biomass_observed[row_select]) %>% mutate(species = factor(species, 
        levels = species), is_observed = !is.na(observed) & observed > 
        0, observed = case_when(is_observed ~ observed, !is_observed ~ 
        model), ratio = model/observed)
    if (sum(dummy$is_observed) == 0) {
        cat(paste("There are no observed biomasses to compare to model,", 
            "only plotting model biomasses.", sep = "\n"))
    }
    if (!show_unobserved) {
        dummy <- filter(dummy, is_observed)
    }
    if (return_data == TRUE) 
        return(dummy)
    tre <- round(sum(abs(1 - dummy$ratio)), digits = 3)
    caption <- paste0("Total relative error = ", tre)
    if (any(!dummy$is_observed)) {
        caption <- paste(caption, "\n Open circles represent species without biomass observation.")
    }
    if (ratio == FALSE) {
        gg <- ggplot(data = dummy, aes(x = observed, y = model, 
            colour = species, shape = is_observed)) + geom_abline(aes(intercept = 0, 
            slope = 1), colour = "purple", linetype = "dashed", 
            size = 1.3) + geom_point(size = 3) + labs(y = "model biomass [g]") + 
            coord_cartesian(ylim = range(dummy$model, dummy$observed))
    }
    else {
        gg <- ggplot(data = dummy, aes(x = observed, y = ratio, 
            colour = species, shape = is_observed)) + geom_hline(aes(yintercept = 1), 
            linetype = "dashed", colour = "purple", 
            size = 1.3) + geom_point(size = 3) + labs(y = "model biomass / observed biomass") + 
            coord_cartesian(ylim = range(dummy$ratio))
    }
    gg <- gg + labs(x = "observed biomass [g]", caption = caption) + 
        scale_colour_manual(values = getColours(params)[dummy$species]) + 
        scale_shape_manual(values = c(`TRUE` = 19, `FALSE` = 1)) + 
        guides(shape = "none")
    if (log_scale == TRUE & ratio == FALSE) {
        gg = gg + scale_x_log10() + scale_y_log10()
    }
    if (log_scale == TRUE & ratio == TRUE) {
        gg = gg + scale_x_log10()
    }
    if (labels == TRUE) {
        gg = gg + ggrepel::geom_label_repel(aes(label = species), 
            box.padding = 0.35, point.padding = 0.5, segment.color = "grey50", 
            show.legend = FALSE, max.overlaps = Inf, seed = 42)
    }
    gg
}
```


```{r}
plotBiomassObservedVsModelCustom(sim_optim)
```


Use tuneParams() to investigate the species with erepro values too high
```{r}
params_tuned_v01 <- tuneParams(params_optim)

params_tuned_v01@species_params$erepro

params_tuned_v01 <- steady(params_tuned_v01)
```

```{r}
# create set of params for the optimisation process
tic()

params_optim <- params_tuned_v01

vary <- c(log10(params_optim@species_params$R_max),
          params_optim@species_params$erepro,
          params_optim@species_params$interaction_resource)

params_optim<-setParams(params_optim)

# set up workers
noCores <- parallel::detectCores() - 1 # keep some spare core
cl <- parallel::makeCluster(noCores, setup_timeout = 0.5)
setDefaultCluster(cl = cl)
clusterExport(cl, varlist = "cl",envir=environment())
clusterEvalQ(cl, {
  library(mizerExperimental)
  library(optimParallel)
})

optim_result <- optimParallel::optimParallel(par=vary,getErrorCustom,params=params_optim, 
                                             dat = params_optim@species_params$biomass_observed, method ="L-BFGS-B", 
                                             lower=c(rep(-15,15),rep(1e-7,15),rep(.1,15)),
                                             upper= c(rep(15,15),rep(0.99,15),rep(.99,15)),
                                             parallel=list(loginfo=TRUE, forward=TRUE))
stopCluster(cl)

toc()

saveRDS(optim_result, file="optim_result_v01.RDS")
# optim_result <- readRDS("optim_result_v00.RDS")
```


```{r}
#put these new vals intospecies_params and go back to the top of this page to re-check the calibration 
species_params(params_optim)$R_max<-10^optim_result$par[1:15]
species_params(params_optim)$erepro<-optim_result$par[16:30]
species_params(params_optim)$interaction_resource <-optim_result$par[31:45]

species_params(params_optim)$R_max
species_params(params_optim)$erepro
species_params(params_optim)$interaction_resource
```


```{r}
sim_optim_v02 <- project(params_optim, t_max = 1000)
plotBiomass(sim_optim_v02)
```

```{r}
params_v03 <- steady(params_optim)
```

```{r}
params_loop <- params_v03 |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady()

params_loop@species_params$erepro
```

```{r}
sim_v02 <- project(params_loop, t_max = 2000)
```



```{r}
plotBiomass(sim_v02)
plotBiomassObservedVsModelCustom(sim_v02)
plotBiomassObservedVsModel(sim_v02)
plotDiet(params_loop)
```
Save progress
```{r}
# saveRDS(params_loop, "params_optim_v01.rds")
```


Try to ween the species of the resource spectra
```{r}
params_loop@resource_params$w_pp_cutoff
params_loop@resource_params$kappa
```

```{r}
resource_params(params_loop)[["w_pp_cutoff"]] <- 10

params_v04 <- setParams(params_loop)

params_v04@resource_params$w_pp_cutoff
```

```{r}
sim_v03 <- project(params_v04, t_max = 200)

plotBiomass(sim_v03)
plotBiomassObservedVsModelCustom(sim_v03)
plotBiomassObservedVsModel(sim_v03)
plotDiet(params_v04)
```

```{r}
getErrorCustom_v02 <- function(vary, params, dat, tol = 0.001, 
    timetorun = 10)
{
  params@species_params$R_max[1:15]<-10^vary[1:15] # R_max for 15 species
  params@species_params$erepro[1:15]<-vary[16:30] # erepro for 15 species
  params@species_params$interaction_resource[1:15] <- vary[31:45] # interaction_resource for 15 species
  params@resource_params$w_pp_cutoff[1] <- vary[46] # interaction_resource for 15 species
  params <- setParams(params)
  # interaction <- params@interaction
  # interaction[] <- matrix(vary[28:108],nrow = 9) # stop at 54 if looking only at 3 biggest species
  
  # params <- setInteraction(params,interaction)
    params <- projectToSteady(params, distance_func = distanceSSLogN, 
        tol = tol, t_max = 200, return_sim = F)
    
    sim <- project(params, t_max = timetorun, progress_bar = F)
    
    sim_biomass = rep(0, length(params@species_params$species))
    
        cutoffLow <- params@species_params$biomass_cutoffLow
    if (is.null(cutoffLow)) 
        cutoffLow <- rep(0, no_sp)
    cutoffLow[is.na(cutoffLow)] <- 0
    
        cutoffHigh <- params@species_params$biomass_cutoffHigh
    if (is.null(cutoffHigh)) 
        cutoffHigh <- rep(0, no_sp)
    cutoffHigh[is.na(cutoffHigh)] <- 0
        
    for (j in 1:length(sim_biomass)) {
        sim_biomass[j] = sum((sim@n[dim(sim@n)[1],j,] * params@w * 
            params@dw)[params@w >= cutoffLow[j] & cutoffHigh[j] >= params@w])
    }
    
     pred <- log(sim_biomass)
    dat <- log(dat)
    discrep <- pred - dat
    discrep <- (sum(discrep^2))
    return(discrep)
}
    
```


```{r}
# create set of params for the optimisation process
tic()

params_optim <- params_v04

vary <- c(log10(params_optim@species_params$R_max),
          params_optim@species_params$erepro,
          params_optim@species_params$interaction_resource,
          params_optim@resource_params$w_pp_cutoff)

params_optim<-setParams(params_optim)

# set up workers
noCores <- parallel::detectCores() - 1 # keep some spare core
cl <- parallel::makeCluster(noCores, setup_timeout = 0.5)
setDefaultCluster(cl = cl)
clusterExport(cl, varlist = "cl",envir=environment())
clusterEvalQ(cl, {
  library(mizerExperimental)
  library(optimParallel)
})

optim_result <- optimParallel::optimParallel(par=vary,getErrorCustom_v02,params=params_optim, 
                                             dat = params_optim@species_params$biomass_observed, method ="L-BFGS-B", 
                                             lower=c(rep(-15,15),rep(1e-7,15),rep(.1,15), 0.1),
                                             upper= c(rep(15,15),rep(0.99,15),rep(.99,15),1000),
                                             parallel=list(loginfo=TRUE, forward=TRUE))
stopCluster(cl)

toc()

saveRDS(optim_result, file="optim_result_v02.RDS")
# optim_result <- readRDS("optim_result_v00.RDS")
```



```{r}
#put these new vals intospecies_params and go back to the top of this page to re-check the calibration 
species_params(params_optim)$R_max<-10^optim_result$par[1:15]
species_params(params_optim)$erepro<-optim_result$par[16:30]
species_params(params_optim)$interaction_resource <-optim_result$par[31:45]
resource_params(params_optim)$w_pp_cutoff <- optim_result$par[46]

species_params(params_optim)$R_max
species_params(params_optim)$erepro
species_params(params_optim)$interaction_resource
```


```{r}
sim_v04 <- project(params_optim, t_max = 1000)

plotBiomass(sim_v04)
plotBiomassObservedVsModelCustom(sim_v04)
plotBiomassObservedVsModel(sim_v04)
plotDiet(params_optim)
```

```{r}
params_steady <- params_optim

initialN(params_steady) <- sim_v04@n[dim(sim_v04@n)[1],,]

plotDiet(params_steady)

sim_v05 <- project(params_steady, t_max = 100)

plotBiomass(sim_v05)

plotGrowthCurves(sim_v05, species_panel = T)
```

```{r}
params_tuned_v02 <- tuneParams(params_steady)

params_tuned_v02 <- steady(params_tuned_v02)

params_tuned_v02@species_params$erepro
```
species        w_min     w_mat    w_max beta k_vb  h min_depth max_depth water.column.use p_time_prydz pc_annual_offspring
5   salps 3.162278e-05 0.2511886 25.11886 1000   NA 33         0       500          non DVM            1                  NA
  biomass_observed
5            0.652
```{r}
species_params <- data.frame(
    species = "salps",
    w_min = 3.162278e-05,
    w_max = 25.11886,
    w_mat = 0.2511886,
    beta = 1000000,
    sigma = 2,
    biomass_observed = 0.652,
    k_vb = 0.4,
    yield_observed = 0,
    biomass_cutoff = 0.1,
    biomass_cutoffLow = 0.1,
    biomass_cutoffHigh = 25.11886
)

params_v05 <- addSpecies(params_tuned_v02, species_params)
plotSpectra(params_v05)

params_v05@species_params$erepro

```


```{r}
# function running tuneParams function in a row for a quick start to a calibration
fastCalib <- function(params, match = F)
{
params <- calibrateBiomass(params) # changes kappa and rmax
if(match) params <- matchBiomasses(params) # set rmax to inf and adjust erepro
params <- steady(params, tol = 0.001)
sim <- project(params, t_max = 1000)
return(sim)
}
```


```{r}
sim_v06 <- fastCalib(params_v05)

plotBiomass(sim_v06)
plotDiet(params_v05)
```

```{r}
so_theta <- as.matrix(params_v05@interaction)

salp_row <- c(1,
0.1250000,
0.1250000,
0.1373626,
0.0500000,
0.1000000,
0.2500000,
0.2500000,
0.1000000,
0.1666667,
0.1666667,
0.0500000,
0.2000000,
0.1250000,
0.2000000,
1)
```


```{r}
so_theta[16,] # automated salps interaction when added using addSpecies()

so_theta[16,] <- salp_row

so_theta[,16] # automated salps interaction when added using addSpecies()

so_theta[,16] <- salp_row

so_theta
```


```{r}
params_v06 <- setParams(params_v05, interaction = so_theta)

params_v06@interaction
```


```{r}
sim_v07 <- fastCalib(params_v06)

plotBiomass(sim_v07)
```

```{r}
initialN(params_v06) <- sim_v07@n[dim(sim_v07@n)[1],,]
```


```{r}
sim_v08 <- project(params_v06, t_max = 100)
plotBiomass(sim_v08)
plotDiet(params_v06)
```

Fill in missing and adjust beta values for zooplankton groups using Heneghan et al. 2020 (10.1016/j.ecolmodel.2020.109265)

Need a value for microzooplankton
microzooplankton (in McCormack et al. 2020) is composed of Heterotrophic dinoflagellates, tintinnids, ciliates, copepod nauplii
Heneghan et al. log10PPMR values for:
Hetero.Flagellates = 0.2–0.72 -> 0.46
Hetero.Ciliates = 2.5–2.9 -> 2.7
Mean: (2.7+0.46)/2 = 1.58
10^1.58 = 38.01894

Need to adjust values for mesozoo, other macrozoo, euphausiids, salps

Heneghan et al. log10PPMR values (midpoints for range) for:
salps = 6.8–11.7 -> 9.25
10^9.25 = 1778279410

euphausiids = 6.6–7.8 -> 7.2
10^7.2 = 15848932

mesozoo (copepods)
Omni.Cop. = 3.6–4.6 -> 4.1
Carn.Cop. = 0.8–1.9 -> 1.35
Mean: (4.1+1.35)/2 = 2.725
10^2.725 = 530.8844

other macrozoo ()
Chaetognaths = 1.9–3.4 -> 2.65
10^2.65 = 446.6836

```{r}
params_tuned_v03 <- tuneParams(params_v06)
params_tuned_v04 <- tuneParams(params_tuned_v03) # updated salps PPMR to match empirical estimates from Heneghan et al 2020
```


```{r}
getErrorCustom_v3 <- function(vary, params, dat, tol = 0.001, 
    timetorun = 10)
{
  params@species_params$R_max[1:16]<-10^vary[1:16] # R_max for 15 species
  params@species_params$erepro[1:16]<-vary[17:32] # erepro for 15 species
  params@species_params$interaction_resource[1:16] <- vary[33:48] # interaction_resource for 15 species
  params <- setParams(params)
  # interaction <- params@interaction
  # interaction[] <- matrix(vary[28:108],nrow = 9) # stop at 54 if looking only at 3 biggest species
  
  # params <- setInteraction(params,interaction)
    params <- projectToSteady(params, distance_func = distanceSSLogN, 
        tol = tol, t_max = 200, return_sim = F)
    
    sim <- project(params, t_max = timetorun, progress_bar = F)
    
    sim_biomass = rep(0, length(params@species_params$species))
    
        cutoffLow <- params@species_params$biomass_cutoffLow
    if (is.null(cutoffLow)) 
        cutoffLow <- rep(0, no_sp)
    cutoffLow[is.na(cutoffLow)] <- 0
    
        cutoffHigh <- params@species_params$biomass_cutoffHigh
    if (is.null(cutoffHigh)) 
        cutoffHigh <- rep(0, no_sp)
    cutoffHigh[is.na(cutoffHigh)] <- 0
        
    for (j in 1:length(sim_biomass)) {
        sim_biomass[j] = sum((sim@n[dim(sim@n)[1],j,] * params@w * 
            params@dw)[params@w >= cutoffLow[j] & cutoffHigh[j] >= params@w])
    }
    
     pred <- log(sim_biomass)
    dat <- log(dat)
    discrep <- pred - dat
    discrep <- (sum(discrep^2))
    return(discrep)
}
```




```{r}
# create set of params for the optimisation process
tic()

params_optim <- params_tuned_v04

vary <- c(log10(params_optim@species_params$R_max),
          params_optim@species_params$erepro,
          params_optim@species_params$interaction_resource)

params_optim<-setParams(params_optim)

# set up workers
noCores <- parallel::detectCores() - 1 # keep some spare core
cl <- parallel::makeCluster(noCores, setup_timeout = 0.5)
setDefaultCluster(cl = cl)
clusterExport(cl, varlist = "cl",envir=environment())
clusterEvalQ(cl, {
  library(mizerExperimental)
  library(optimParallel)
})

optim_result <- optimParallel::optimParallel(par=vary,getErrorCustom_v3,params=params_optim, 
                                             dat = params_optim@species_params$biomass_observed, method ="L-BFGS-B", 
                                             lower=c(rep(-15,16),rep(1e-7,16),rep(.1,16)),
                                             upper= c(rep(15,16),rep(0.99,16),rep(.99,16)),
                                             parallel=list(loginfo=TRUE, forward=TRUE))
stopCluster(cl)

toc()

saveRDS(optim_result, file="optim_result_v03.RDS")
# optim_result <- readRDS("optim_result_v00.RDS")
```


```{r}
#put these new vals intospecies_params and go back to the top of this page to re-check the calibration 
species_params(params_optim)$R_max<-10^optim_result$par[1:16]
species_params(params_optim)$erepro<-optim_result$par[17:32]
species_params(params_optim)$interaction_resource <-optim_result$par[33:48]

species_params(params_optim)$R_max
species_params(params_optim)$erepro
species_params(params_optim)$interaction_resource
```

```{r}
sim_v09 <- project(params_optim, t_max = 200)
plotBiomass(sim_v09)
```


```{r}
params_v07 <- params_optim
initialN(params_v07) <- sim_v09@n[dim(sim_v09@n)[1],,]
```


```{r}
sim_v10 <- project(params_v07, t_max = 200)
plotBiomass(sim_v10)
plotDiet(params_v07)
```

```{r}
initialN(params_v07) <- sim_v10@n[dim(sim_v10@n)[1],,]
```



```{r}
sim_v11 <- project(params_v07, t_max = 200)
plotBiomass(sim_v11)
```


```{r}
params_tuned_v05 <- tuneParams(params_v07)

params_tuned_v05 <- steady(params_tuned_v05)

params_tuned_v05@species_params$erepro
```


```{r}
# saveRDS(params_tuned_v05, "params_optim_v02.rds")

params_tuned_v05 <- readRDS("params_optim_v02.rds")
```



```{r}
sim_v12 <- project(params_tuned_v05, t_max = 500)

initialN(params_tuned_v05) <- sim_v12@n[dim(sim_v12@n)[1],,]

sim_v13 <- project(params_tuned_v05, t_max = 500)
```

```{r}
plotlyBiomass(sim_v13)
plotBiomassObservedVsModel(sim_v13)
plotBiomassObservedVsModelCustom(sim_v13)
```

Gradually reducing resource maximum size in tuneParams(), as doing it directly in the setParams() route will tell you the w_pp_cutoff has changed, but it will still incorporate a background resource up to the w_pp_cutoff that was originally used in newMultispeciesParams()

```{r}
params_tuned_v06 <- tuneParams(params_tuned_v05)

params_tuned_v06 <- steady(params_tuned_v06)

params_tuned_v06@species_params$erepro
params_tuned_v06@species_params$R_max

```
```{r}
params_tuned_v10 <- tuneParams(params_tuned_v06)

params_tuned_v10 <- steady(params_tuned_v10)

params_tuned_v10@species_params$erepro
params_tuned_v10@species_params$R_max

params_tuned_v10@resource_params$w_pp_cutoff

```


```{r}
# saveRDS(params_tuned_v06, "params_optim_v02_w_pp_100.rds")
# saveRDS(params_tuned_v10, "params_optim_v03_w_pp_100.rds")

```


```{r}
# create set of params for the optimisation process
tic()

params_optim <- params_tuned_v10

vary <- c(log10(params_optim@species_params$R_max),
          params_optim@species_params$erepro,
          params_optim@species_params$interaction_resource)

params_optim<-setParams(params_optim)

# set up workers
noCores <- parallel::detectCores() - 1 # keep some spare core
cl <- parallel::makeCluster(noCores, setup_timeout = 0.5)
setDefaultCluster(cl = cl)
clusterExport(cl, varlist = "cl",envir=environment())
clusterEvalQ(cl, {
  library(mizerExperimental)
  library(optimParallel)
})

optim_result <- optimParallel::optimParallel(par=vary,getErrorCustom_v3,params=params_optim,
                                             dat = params_optim@species_params$biomass_observed, method ="L-BFGS-B",
                                             lower=c(rep(-15,16),rep(1e-7,16),rep(.1,16)),
                                             upper= c(rep(15,16),rep(0.99,16),rep(.99,16)),
                                             parallel=list(loginfo=TRUE, forward=TRUE))
stopCluster(cl)

toc()

saveRDS(optim_result, file="optim_result_v04.RDS")

# optim_result <- readRDS("optim_result_v04.RDS")
```


```{r}
#put these new vals intospecies_params and go back to the top of this page to re-check the calibration 
species_params(params_optim)$R_max<-10^optim_result$par[1:16]
species_params(params_optim)$erepro<-optim_result$par[17:32]
species_params(params_optim)$interaction_resource <-optim_result$par[33:48]

species_params(params_optim)$R_max
species_params(params_optim)$erepro
species_params(params_optim)$interaction_resource
```



```{r}
sim_v14 <- project(params_optim, t_max = 2000)
plotBiomass(sim_v14)
```


```{r}
params_v08 <- params_optim
initialN(params_v08) <- sim_v14@n[dim(sim_v14@n)[1],,]
```


```{r}
params_tuned_v07 <- tuneParams(params_v08)

params_tuned_v07 <- steady(params_tuned_v07)

params_tuned_v07@species_params$erepro
params_tuned_v07@species_params$R_max

# saveRDS(params_tuned_v07, file="params_optim_v04_w_pp_100.rds")

```


```{r}
sim_v15 <- project(params_tuned_v07, t_max = 2000)
plotBiomass(sim_v15)
plotDiet(params_tuned_v07)
```


```{r}
initialN(params_tuned_v07) <- sim_v15@n[dim(sim_v15@n)[1],,]

sim_v16 <- project(params_tuned_v07, t_max = 500)
plotlyBiomass(sim_v16)
plotBiomassObservedVsModel(sim_v16)
```

```{r}
params_loop <- params_tuned_v07 |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady() |>
    matchBiomasses() |> steady() |> matchBiomasses() |> steady()

params_loop@species_params$erepro
plotBiomassObservedVsModel(params_loop)

```


```{r}
sim_v17 <- project(params_loop, t_max = 500)
plotlyBiomass(sim_v17)
plotBiomassObservedVsModel(sim_v17)
plotBiomassObservedVsModelCustom(sim_v17)
plotBiomassRelative(sim_v17)
plotDiet(params_loop)
```

```{r}
initialN(params_loop) <- sim_v17@n[dim(sim_v17@n)[1],,]
```


```{r}
sim_v18 <- project(params_loop, t_max = 1000)
plotlyBiomass(sim_v18)
plotBiomassObservedVsModel(sim_v18)
plotBiomassObservedVsModelCustom(sim_v18)
plotBiomassRelative(sim_v18)
plotDiet(params_loop)
```











